Quantifying Effort in American Football

Background and Motivation

• Not all rushing yards are equal

• Traditional stats miss the “how” behind yards gained

• Previous research has explored athletes reaching theoretical max capacity ¹

• Can we “measure” effort using tracking data?

• Multiple ways of evaluating effort

  • Intangible and subjective
  • Depends on player position, defense faced, game context, snap count/play volume, play call/assignment

Our Data: 2022 NFL Season

• Game, play, player, tracking data from Weeks 1-9 ¹

• Running plays where a running back (RB) is the ball carrier

• Trimmed each play to frames between handoff and end of play

Motivation: acceleration-speed (AS) profiles tell us about players’ acceleration capacities at different speeds (Morin et al., 2021)

Effort = percentage of a player’s points (frames) above the relaxed regression line

• Biased toward backups because of lower sample size…or are they just working harder? Are starters fatigued or pacing themselves?

• Unrealistic theoretical max speeds

Quantile regression provides a better way of evaluating players’ acceleration capacities

New ways of looking at effort

Concerns and limitations

• Problem with qgam: some players have more than 1% of points above 99th percentile

  • Account for this without penalizing players who simply have large raw number of points above the line

• Potential fix: Effort v3 = \(\left(\sum\limits_{i=1}^{n_{\text{below}}} {\frac{1}{1+d_i}} + \frac{.01}{\text{% of points above } P_{99}}\right)\bigg/n_{\text{total}}\)

• Unsure about threshold for relaxed percentile line in Effort v2

Plan of action

• Define research question and scope ✅

• Data cleaning and preprocessing, EDA ✅

• Develop basic AS profiles for players ✅

• Obtain effort metric(s) with quantile regression ⏳

• Evaluate effort metric(s) by correlating it to effort-related outcomes